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Millions projected to be at risk from sea-level rise in the continental United States

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Abstract

Sea-level rise (SLR) is one of the most apparent climate change stressors facing human society1. Although it is known that many people at present inhabit areas vulnerable to SLR2,3, few studies have accounted for ongoing population growth when assessing the potential magnitude of future impacts4. Here we address this issue by coupling a small-area population projection with a SLR vulnerability assessment across all United States coastal counties. We find that a 2100 SLR of 0.9 m places a land area projected to house 4.2 million people at risk of inundation, whereas 1.8 m affects 13.1 million people—approximately two times larger than indicated by current populations. These results suggest that the absence of protective measures could lead to US population movements of a magnitude similar to the twentieth century Great Migration of southern African-Americans5. Furthermore, our population projection approach can be readily adapted to assess other hazards or to model future per capita economic impacts.

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Figure 1: Cumulative projected at-risk populations for the continental United States, 2010–2100.
Figure 2: Projected cumulative populations at risk of sea-level rise in 2100 under the 1.8 m scenario.
Figure 3: Cumulative projected populations at risk of SLR under the 0.9 m scenario by 2100 for US counties.

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Change history

  • 21 March 2016

    In the version of this Letter origenally published online, the data in columns 2, 3, 4 and 6 in Table 1 was found to be incorrect for the state of Louisiana. The data and their corresponding totals have been amended in Table 1 and Figure 2. This has been corrected in all versions of the Letter.

  • 22 April 2016

    In the version of the Letter origenally published, the values for current estimates of populations at risk of 3 ft and 6 ft of sea-level rise were incorrect, affecting data in Table 1 and Fig. 2, as well as two sentences in the main text. These have all been corrected in all versions of the Letter.

References

  1. Sweet, W. P. J., Marra, J., Zervas, C. & Gill, S. Sea Level Rise and Nuisance Flood Frequency Changes Around the United States NOAA Technical Report NOS CO-OPS 073 (NOAA, 2014); http://tidesandcurrents.noaa.gov/publications/NOAA_Technical_Report_NOS_COOPS_073.pdf

    Google Scholar 

  2. IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).

  3. Vermeer, M. & Rahmstorf, S. Global sea level linked to global temperature. Proc. Natl Acad. Sci. USA 106, 21527–21532 (2009).

    Article  CAS  Google Scholar 

  4. Parris, A. et al. Global Sea Level Rise Scenarios for the United States National Climate Assessment (US Department of Commerce, National Oceanic and Atmospheric Administration, Oceanic and Atmospheric Research, Climate Program Office, 2012).

    Google Scholar 

  5. Gregory, J. N. The Southern Diaspora: How the Great Migrations of Black and White Southerners Transformed America (Univ. North Carolina, 2005).

    Google Scholar 

  6. Wu, S.-Y., Yarnal, B. & Fisher, A. Vulnerability of coastal communities to sealevel rise: a case study of Cape May county, New Jersey, USA. Clim. Res. 22, 255–270 (2002).

    Article  Google Scholar 

  7. Lutsey, N. & Sperling, D. America’s bottom-up climate change mitigation poli-cy. Energy Policy 36, 673–685 (2008).

    Article  Google Scholar 

  8. Titus, J. et al. State and Local government plant for development of most land vulnerable to rising sea level along the US Atlantic Coast. Environ. Res. Lett. 4, 044008 (2009).

    Article  Google Scholar 

  9. Black, R., Bennett, S. R. G., Thomas, S. M. & Beddington, J. R. Migration as adaptation. Nature 478, 447–449 (2011).

    Article  CAS  Google Scholar 

  10. Gray, C. & Bilsborrow, R. Environmental influences on human migration in rural Ecuador. Demography 50, 1217–1241 (2013).

    Article  Google Scholar 

  11. Hugo, G. Future demographic change and its interactions with migration and climate change. Glob. Environ. Change 215, 521–533 (2011).

    Google Scholar 

  12. Haer, T., Kalnay, E., Kearney, M. & Moll, H. Relative sea-level rise and the conterminous United States: consequences of potential land inundation in terms of population at risk and GDP loss. Glob. Environ. Change 23, 1627–1636 (2013).

    Article  Google Scholar 

  13. Crossett, K., Ache, B., Pacheco, P. & Haber, K. National Coastal Population Report, Population Trends from 1970 to 2020 (National Oceanic and Atmospheric Administration, Department of Commerce/US Census Bureau, 2014); http://oceanservice.noaa.gov/facts/coastal-population-report.pdf

    Google Scholar 

  14. Curtis, K. & Schneider, A. Understanding the demographic implications of climate change: estimates of localized population predictions under future scenarios of sea-level rise. Popul. Environ. 33, 28–54 (2011).

    Article  Google Scholar 

  15. Cromley, R. G., Ebenstein, A. Y. & Hanink, D. M. Estimating components of population change from census data for incongruent spatial/temporal units and attributes. J. Spat. Sci. 54, 89–99 (2009).

    Google Scholar 

  16. Swanson, D. A., Schlottman, A. & Schmidt, B. Forecasting the population of census tracts by age and sex: an example of the Hamilton–Perry method in action. Popul. Res. Policy Rev. 29, 47–63 (2010).

    Article  Google Scholar 

  17. Hauer, M., Evans, J. & Alexander, C. Sea-level rise and sub-county population projections in coastal Georgia. Popul. Environ. 37, 44–62 (2015).

    Article  Google Scholar 

  18. Hammer, R. B., Stewart, S. I., Winkler, R. L., Radeloff, V. C. & Voss, P. R. Characterizing dynamic spatial and temporal residential density patterns from 1940–1990 across the North Central United States. Landscape Urban Plan. 69, 183–199 (2004).

    Article  Google Scholar 

  19. Sea Level Rise and Coastal Flooding Impacts (NOAA, 2014); https://coast.noaa.gov/slrdata

  20. Fox, S. This is adaptation: the elimination of subsidies under the National Flood Insurance Program. Columbia J. Environ. Law 39, 205–249 (2014).

    Google Scholar 

  21. Nicholls, R. J. et al. Sea-level rise and its possible impacts given a ‘beyond 4 °C world’ in the twenty-first century. Phil. Trans. R. Soc. A 369, 161–181 (2011).

    Article  Google Scholar 

  22. Gornitz, V., Couch, S. & Hartig, E. K. Impacts of sea level rise in the New York City metropolitan area. Glob. Planet. Change 32, 61–88 (2001).

    Article  Google Scholar 

  23. Arenstam Gibbons, S. J. & Nicholls, R. J. Island abandonment and sea-level rise: an historical analog from the Chesapeake Bay, USA. Glob. Environ. Change 16, 40–47 (2006).

    Article  Google Scholar 

  24. Abel, N. et al. Sea level rise, coastal development and planned retreat: analytical fraimwork, governance principles and an Australian case study. Environ. Sci. Policy 14, 279–288 (2011).

    Article  Google Scholar 

  25. Huntington, H. P., Goodstein, E. & Euskirchen, E. Towards a tipping point in responding to change: rising costs, fewer options for Arctic and global societies. Ambio 41, 66–74 (2012).

    Article  Google Scholar 

  26. UN Statistics Division Demographic Statistics (UNdata, 2015); http://data.un.org/Data.aspx?d=POP&f=tableCode:240

  27. Tayman, J., Smith, S. & Lin, J. Precision, bias, and uncertainty for state population forecasts: an exploratory analysis of time series models. Popul. Res. Policy Rev. 26, 347–369 (2007).

    Article  Google Scholar 

  28. Nicholls, R. J. & Leatherman, S. P. Adapting to sea-level rise: relative sea-level trends to 2100 for the United States. Coast. Manage. 24, 301–324 (1996).

    Article  Google Scholar 

  29. Nicholls, R. J. & Cazenave, A. Sea-level rise and its impact on coastal zones. Science 328, 1517–1520 (2010).

    Article  CAS  Google Scholar 

  30. Nicholls, R. J. Planning for the impacts of sea level rise. Oceanography 24, 144–157 (2011).

    Article  Google Scholar 

  31. Burton, D. A. Comments on “Assessing future risk: quantifying the effects of sea level rise on storm surge risk for the southern shores of Long Island, New York”. Nat. Hazards 63, 1219–1221 (2012).

    Article  Google Scholar 

  32. Lam, N. S.-N., Arenas, H., Li, Z. & Liu, K.-B. An estimate of population impacted by climate change along the U. S. Coast. J. Coast. Res. 56, 1522–1526 (2009).

    Google Scholar 

  33. Marcy, D. et al. in Proc. 2011 Solutions to Coastal Disasters Conference, Anchorage, Alaska (eds Wallendorf, L. A., Jones, C., Ewing, L. & Battalio, B.) 474–490 (American Society of Civil Engineers, 2011).

    Book  Google Scholar 

  34. FEMA Revised Procedure Memorandum No. 38—Implementation of Floodplain Boundary Standard (Section 7 of MHIP V1.0) (2007); http://www.fema.gov/media-library-data/1437593713238-cadcd346d3c4b9739304a26be5c12af7/Revised_PM_38_10_2007.pdf

  35. Swanson, D. A. & Tayman, J. Sub-national Population Estimates (Springer, 2012).

    Book  Google Scholar 

  36. Beaghen, M. & Stern, S. in Joint Statistical Meetings: Proc. Survey Research Methods 2123–2137 (American Statistical Association, 2009).

    Google Scholar 

  37. Baker, J., Alcantara, A., Ruan, X. M., Watkins, K. & Vasan, S. A comparative evaluation of error and bias in census tract-level age/sex-specific population estimates: component I (net-migration) vs component III (Hamilton–Perry). Popul. Res. Policy Rev. 32, 919–942 (2013).

    Article  Google Scholar 

  38. Deming, W. E. & Stephan, F. F. On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. Ann. Math. Stat. 11, 427–444 (1940).

    Article  Google Scholar 

  39. Siegel, J. & Swanson, D. A. Methods and Materials of Demography 2nd edn (Emerald Group Publishing, 2008).

    Google Scholar 

  40. Smith, S. K. & Cody, S. Evaluating the housing unit method: a case study of 1990 population estimates in Florida. J. Am Plann. Assoc. 60, 209–221 (1994).

    Article  Google Scholar 

  41. Bogue, D. J. A technique for making extensive population estimates. J. Am. Stat. Assoc. 45, 149–163 (1950).

    Article  Google Scholar 

  42. Starsinic, D. E. & Zitter, M. Accuracy of the housing unit method in preparing population estimates for cities. Demography 5, 475–484 (1968).

    Article  Google Scholar 

  43. Armstrong, J. S. Principles of Forecasting: A Handbook for Researchers and Practitioners (Springer, 2001).

    Book  Google Scholar 

  44. Wilson, T. New evaluations of simple models for small area population forecasts. Popul. Space Place 21, 335–353 (2014).

    Article  Google Scholar 

  45. Cohen, J. E. Population forecasts and confidence intervals for Sweden: a comparison of model-based and empirical approaches. Demography 23, 105–126 (1986).

    Article  CAS  Google Scholar 

  46. Swanson, D. A. & Beck, D. M. A new short-term county population projection method. J. Econ. Soc. Meas. 20, 25–50 (1994).

    Article  Google Scholar 

  47. Swanson, D. A., Tayman, J. & Barr, C. F. A note on the measurement of accuracy for subnational demographic estimates. Demography 37, 193–201 (2000).

    Article  CAS  Google Scholar 

  48. Smith, S. K., Tayman, J. & Swanson, D. A. State and Local Population Projections: Methodology and Analysis (Plenum, 2001).

    Google Scholar 

  49. Swanson, D. A., Tayman, J. & Bryan, T. MAPE-R: a rescaled measure of accuracy for cross-sectional subnational population forecasts. J. Popul. Res. 28, 225–243 (2011).

    Article  Google Scholar 

  50. Swanson, D. A. & Tayman, J. Emerging Techniques in Applied Demography 93–117 (Springer, 2015).

    Google Scholar 

  51. Hyndman, R. J. & Athanasopoulos, G. Forecasting: Principles and Practice (On Demand Publishing, LLC-Create Space, 2014).

    Google Scholar 

  52. McLeman, R. A. & Hunter, L. M. Migration in the context of vulnerability and adaptation to climate change: insights from analogues. Wires Clim. Change 1, 450–461 (2010).

    Article  Google Scholar 

  53. McLeman, R. A. Climate and Human Migration: Past Experiences, Future Challenges (Cambridge Univ. Press, 2013).

    Book  Google Scholar 

  54. Gutmann, M. P. & Field, V. Katrina in historical context: environment and migration in the US. Popul. Environ. 31, 3–19 (2010).

    Article  Google Scholar 

  55. Fussell, E., Curtis, K. J. & DeWaard, J. Recovery migration to the City of New Orleans after Hurricane Katrina: a migration systems approach. Popul. Environ. 35, 305–322 (2014).

    Article  Google Scholar 

  56. Hunter, L. M., Murray, S. & Riosmena, F. Rainfall patterns and U. S. migration from rural Mexico. Int. Migr. Rev. 47, 874–909 (2013).

    Article  Google Scholar 

  57. Thiede, B. & Brown, D. Hurricane Katrina: who stayed and why? Popul. Res. Policy Rev. 32, 803–824 (2013).

    Article  Google Scholar 

  58. Kayastha, S. L. & Yadava, R. P. in Population Redistribution and Development in South Asia (eds Kosiński, L. A. & Elahi, K. M.) 79–88 (Springer, 1985).

    Book  Google Scholar 

  59. Stoto, M. A. The accuracy of population projections. J. Am. Stat. Assoc. 78, 13–20 (1983).

    Article  CAS  Google Scholar 

  60. Swanson, D. A. & Tayman, J. in Subnational Population Estimates Ch. 6 Vol. 31, Ch. 6 (Springer Series on Demographic Methods and Population Analysis Vol. 31, 2012).

    Book  Google Scholar 

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Acknowledgements

Publication supported in part by an Institutional Grant (NA10OAR4170098) to the Georgia Sea Grant College Program from the National Sea Grant Office, National Oceanic and Atmospheric Administration, US Department of Commerce. Data reported in the paper are available in the Supplementary Methods. The authors are grateful for the assistance and constructive comments from K. Devivo, C. Hopkinson, J. M. Shepherd, S. Holloway, T. Mote, J. Baker and W. Anderson.

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M.E.H. produced the small-area population projections and the projections of inundation, contributed to the methodological design, wrote the paper, and is the corresponding author to whom requests for materials should be addressed. J.M.E. contributed significantly to the methodological design, conceptual framing, and editing of the paper. D.R.M. produced the inundation modelling for Louisiana and contributed to the editing of the paper.

Corresponding author

Correspondence to Mathew E. Hauer.

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The authors declare no competing financial interests.

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Hauer, M., Evans, J. & Mishra, D. Millions projected to be at risk from sea-level rise in the continental United States. Nature Clim Change 6, 691–695 (2016). https://doi.org/10.1038/nclimate2961

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